Adaptive product configuration model

ABSTRACT

The adaptive product conditioning is a computer-implemented method for identifying product configurations that can be provided to customers in reaction to supply imbalances. The methodology uses data mining techniques to collect and analyze business level meta data to coordinate supply and sales goals in terms of optimizing profits or managing product and technology transitions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 11/038,536 filed Jan. 21, 2005, now abandoned.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to supply chain management andproduct offering conditioning and, more particularly, to identifyingalternative products or substituting components that can be provided tocustomers in reaction to supply imbalances.

2. Background Description

Supply-Demand conditioning is a decision process within thesupply-demand planning process that monitors imbalances between supplyand demand and recommends corrective actions before an imbalance becomesa threat to customer service. To resolve an imbalance situation, thedecision maker needs to choose the appropriate corrective action. Theseactions fall into three categories:

-   -   Supply conditioning: Working with suppliers to improve        flexibility in supply to react to customer demand that is never        totally predictable.    -   Demand conditioning: Providing dynamic sales plans that can be        changed in reaction to supply imbalances. Considers pricing        actions and promotions to provide incentives to customers to        choose alternatives.    -   Product offering conditioning: Identifying alternative product        configurations in reaction to supply imbalances. Supported by a        proactive product definition phase that provides more        flexibility to define product configurations.

SUMMARY OF THE INVENTION

The subject invention provides a method that finds product offeringalternatives to better coordinate supply and sales, and developingoptimal build plans that would best utilize component inventories. Thismethod is most appropriate for use in an assembly environment. It wouldnot only enable proactive coordination of supply and sales in terms ofoptimizing profit, but also help manage major product and technologytransitions.

It is an exemplary object of the present invention to provide acomputer-implemented method that obtains meta data from a variety ofdatabases and other information sources that relate to but is notlimited to existing product configurations, marketing and sales goalsand revenue targets, and logistics and provisioning levels.

Another exemplary object of the invention is to identify possiblealternative product offerings to manage supply side imbalances at acomponent or other logistical level.

It is still a further exemplary object to analyze business level as wellas operational level thresholds to rate and select specific productconfigurations that maximize financial goals while minimizing assetliabilities.

It is another object of the invention to provide product configurationdata to the various business level organizations and update the relateddatabases to incorporate the conditioning information.

According to the invention, there is provided a computer-implementedmethodology that assesses a myriad of business and operation level datato maximize revenues and other business goals while minimizing theliabilities associated with supply imbalances and other operational andtactical goals.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 is a block diagram illustrating the Adaptive Product Conditioningmodel according to the invention.

FIG. 2 is a system level diagram of the various components and resourcesaccording to the invention.

FIG. 3 is a block diagram of the elements within the Adaptive ProductConditioning model.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

Referring now to the drawings, and more particularly to FIG. 1, there isa block diagram of the Adaptive Product Conditioning Model according tothe invention which includes a computer-implemented method for takingbusiness and operational data as an input and providing specific productconfigurations which meet business goals such as but not limited tothose identified as part of sales planning, manufacturing planning (alsocalled build planning), and customer support planning.

FIG. 1 shows business databases and other information sources 101 as theinput to the Adaptive Product Configuration Model 102. The type ofinformation obtained by the Adaptive Product Configuration Model 102from the business databases and other information sources 101 wouldinclude but not be limited to technical, financial, logistical, andcustomer data. This data could be classified in many ways to includeinformation defining available physical assets, costs of the availableassets, customer demands for the assets and price points for the assets.

Once these data are obtained and classified, the Adaptive ProductConfiguration Model 102 analyzes the data and produces productconfiguration alternatives. These alternatives are rated based onstrategic and tactical planning requirements and other businessthresholds. The Adaptive Product Configuration Model 102 will thenproduce any one of several outputs. These outputs can be in the form ofreports and/or automatic updates of source information databases. Forexample, a report could be provided to manufacturing 103 that containsdetailed ‘build to’ product configuration data. Manufacturing 103 coulduse this data to construct the new recommended product configurations.Reports (either manual or automated) could also be provided to themarketing/sales 104 organizations to describe the new productconfigurations that would be available for sale. The sales/marketing 104functions within the company could use the data to update sales andmarketing plans and provide targeted promotions and sales incentives tosell the new product configurations. Another possible output would be tothe logistics 105 function within the company. This data could also beeither manual or electronic as in automatic database updating and wouldenable the logistics 105, distribution and other end providerorganizations to reallocate supply to meet the new product configurationsupport and distribution requirements.

Referring now to FIG. 2, the Adaptive Product Configuration Model 102 ofFIG. 1 can be implemented in a local area network 220 within a corporatestructure or can incorporate data from sources located locally orconnected through a wide area network 210 such as, but not limited to,connectivity through the Internet. Likewise, the system for performingthe method, software or firmware containing the instruction set forperforming the method can be processed within servers (225, 226, 227)located on the local area network 220 or servers (213, 212) located onthe wide area network or a combination of these servers (213, 212, 225,226, 227). The data utilized by the method can be stored in databases(211, 228, 229) located in either the local area network 220, wide areanetwork 210 or a combination of these. The product configurationdatabase 229 is shown as a separate element for simplicity. However, theproduct configuration database 229 could be part of any of the databasesaccessible to the application software or firmware that implements theAdaptive Product Configuration Model 102. Finally, additionalinformation can be entered by an operator through terminals (215, 214,221, 222) or could be conveyed verbally to the operator by telephone(216, 223, 224), facsimile machine, or other commonly known means.Modifications to the thresholds and goals used to rate the productconfigurations are an example of the type of data that could be inputmanual through the terminals (215, 214, 221, 222).

FIG. 2 shows a limited number of elements (terminals, telephones,servers, databases); however, the environment is not intended to limitthe structure of the elements and is used only as a means fordescription. Those familiar with the art would understand the variationspossible in networked environments.

A schematic of the Adaptive Product Configuration Model is given in FIG.3. The Adaptive Product Configuration Model would obtain the existingproduct data at step 310. These data could be supplied either manuallyor electronically by the various company organizations such asmanufacturing 303, logistics/provisioning 302, and marketing/sales 301.These data provided by these sources can be but are not limited to:

-   1. engineering specifications for each existing product offered by    the company,-   2. logistics parts and provisioning databases,-   3. financial data such as sales projections, margin targets, revenue    predictions and marketing goals, production costs, distribution    costs, etc., and-   4. customer demand and market trends data.    These data can be provided in the form of an inventory statement and    product configuration rules as well as sales plans and build plans.    Product configuration rules would include but not be limited to    those technical and manufacturing restrictions that define how    various components can be assembled to form various product    configurations. For example, the power consumption requirements of a    specific component could limit the types of assemblies in which a    component could be configured and would thus be included as one of    the product configuration rules. It should be understood by those    skilled in the art that the invention is not limited to the example    product configuration rules described for this invention. In    addition to inventory statements and product configuration rules,    design thresholds would also be established either as part of the    initial implementation or entered as updates during the operation of    the model.

For example, a component supplier is planning to transition from a 14″XGA panel to a 15″ XGA panel and will no longer supply or support the14″ XGA. To accelerate its customer' acceptance of 15″ XGA panels, thevendor has offered a computer manufacturing company a volume discount ofsuch panels. The computer manufacturing company using the AdaptiveProduct Configuration Model would analyze their existing productconfigurations by assessing the inventory statements and applying theconfiguration rules to develop alternate configurations at step 320using the discounted 15″ XGA.

The Adaptive Product Configuration Model would then compare and analyzethe alternatives at step 330 to create a set of recommended productconfigurations. Step 330 would perform the analysis using variouscriteria selected from a group to include: maximizing revenue of saidbuild plan; maximizing profitability of said build plan; minimizingliability costs for under-utilizing said inventory statement; minimizingpenalty costs for violating desired customer services levels; minimizingpenalty costs for deviating from sales plan of said set of existingproduct configurations; and maximizing a goodness value function of theproduct configuration.

The goodness value function is a means for rating the alternativeproduct configurations. The goodness value function uses one or more ofthe following criterion to rank the proposed alternatives: profitabilityof said product configurations; competitive advantage gain of saidproduct configuration; marketability of said product configurations;compatibility of said product configurations with said set of existingproduct configurations; and cannibalization of a new productconfiguration with said set of existing product configurations.

Step 340 would then develop build plans for the recommendedconfigurations to include but not be limited to contractual agreementsdescribing upside and downside volume flexibility of supply-committedcomponent inventories; product substitution rules defining one or morealternative products for an existing end product; and upside demandpotential relative to a top-level sales plan.

Finally, the product configuration database would be updated at step340. These build plans could be distributed to various organizationswithin the company such as manufacturing, sales/marketing, and logisticsand distribution as described previously relative to FIG. 1.

An Illustrative Example

To further illustrate the invention, the following describes a problemsolved by the invention and contrasts it with the prior art. While theexample refers to the planning and manufacturing of personal computers(PCs) the invention disclosed is not limited to PCs but would beunderstood by those skilled in the art to include any parts and/orcomponents corresponding to any product build.

Consider the case of three PC product families, F1, F2 and F3, whichmight represent low-end, mid-range, and high-end portable computers.Each product family comprises one or more pre-defined productconfigurations, P1 to P10, as displayed in Table 1. The productconfigurations P1 to P4 belong to product family F1, P5 to P7 belong tofamily F2, and P8 to P10 belong to family F3. The table furtherindicates the bill-of-materials of each product configuration. Forexample, the assembly of product configuration P1 requires one unit of14″ XGA panel, one unit of 20.0 GB hard drive, one unit of DVD opticaldrive, and one unit of wireless hardware WiFi B. The three productfamilies differ by the size and type of panel used in theirbill-of-materials: configurations in family F1 require a 14″ XGA panel,whereas configurations in family F2 require a 15″ XGA panel, andconfigurations in family F3 require a 15″ SXGA+ panel.

TABLE 1 Bill-of-materials of original product configurations. Family F1Family F2 Family F3 PC Components P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Panels14″ XGA 1 1 1 1 15″ XGA 1 1 1 15″ 1 1 1 SXGA+ Hard 20.0 GB 1 1 1 Drives40.0 GB 1 60.0 GB 1 1 1 80.0 GB 1 1 1 Optical DVD 1 1 1 Drives CD-RW 1 11 1 Combo 1 1 1 Wireless WiFi A 1 1 1 1 1 Hardware WiFi B 1 1 1 1 1

Next, it is assumed that the demand forecast is 1,000 units for each ofthe product configurations P1 to P10. (The demand forecast is a salesprojection over a pre-defined planning horizon such as a week, month orquarter).

To determine the corresponding component supply, the top-level demandforecast is exploded through the bill-of-materials in a standardMRP-type calculation. Table 2 shows the component supply requirementspertaining to the top-level demand forecast. The supply requirements arepassed along to component suppliers in a supply-demand collaborationprocess, and the manufacturing company requests a supply commitment toits supply requirements. The supply commitment indicates a supplier'scapability to deliver to the manufacturer's supply requirements. Theright-most column in Table 2 shows a sample supply commitment.

TABLE 2 Component supply requirements and supply commitment. SupplySupply PC Components Requirement Commitment Panels 14″ XGA 4,000 2,00015″ XGA 3,000 8,000 15″ SXGA+ 3,000 3,000 Hard Drives 20.0 GB 3,0003,600 40.0 GB 1,000 1,500 60.0 GB 3,000 3,600 80.0 GB 3,000 4,000Optical DVD 3,000 4,000 Drives CD-RW 4,000 4,800 Combo 3,000 3,600Wireless WiFi A 5,000 6,500 Hardware WiFi B 5,000 6,000

Comparing the supply requirements with the supply commitment indicates asupply constraint on the 14″ XGA panels. To mitigate the supplyconstraint, the panel supplier committed a higher than requested supplyvolume for the 15″ XGA panel, i.e., 8,000 units versus 3,000 units thatwere requested.

For this data, conventional Material Requirements Planning (MRP) systemsand other support tools for helping companies decide what to build wouldmatch the supply to the proposed demand forecast and provide anoptimized build plan. A build plan created by such a tool is displayedin Table 3. Notice that due to the limited supply of 14″ XGA panels thebuild plan results in 1,000 backorders for product configurations P1 andP4.

TABLE 3 Build plan generated by a conventional MRP system. Family F1Family F2 Family F3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Demand 1000 1000 10001000 1000 1000 1000 1000 1000 1000 Forecast Production — 1000 1000 —1600 1000 1600 1000 1000 1000 Plan Backorders 1000 — — 1000 — — — — — —Backorder $50,000 — — $50,000 — — — — — — costs

Table 4 displays the consumed and excess component supply that resultsfrom the build plan shown in Table 3.

TABLE 4 Consumed supply, excess supply, and liability costs for thebuild plan generated by a conventional MRP system. Supply SupplyConsumed Excess Liability PC Components Requirement Commitment SupplySupply Costs Panels 14″ XGA 4,000 2,000 2,000 — — 15″ XGA 3,000 8,0004,200 3,800 $3,800 15″ SXGA+ 3,000 3,000 3,000 — — Hard Drives 20.0 GB3,000 3,600 2,000 1,600 $1,600 40.0 GB 1,000 1,500 — 1,500 $1,500 60.0GB 3,000 3,600 3,600 — — 80.0 GB 3,000 4,000 3,600 400 $400 Optical DVD3,000 4,000 1,000 3,000 $3,000 Drives CD-RW 4,000 4,800 4,600 200 $200Combo 3,000 3,600 3,600 — — Wireless WiFi A 5,000 6,500 6,200 300 $300Hardware WiFi B 5,000 6,000 3,000 3,000 $3,000

For illustrative purposes, it can be assumed that the total cost of abuild plan is the sum of a) the cost of unfilled demand (backordercosts) and b) the cost of unused component inventory (liability costs).If the backorder cost per unit of unfilled demand is $50 and theliability cost per unit of unused component inventory is $1, the abovebuild plan generates a backorder cost of $100,000 and a liability costof $13,800 as shown in Tables 3 and 4. The total cost of the build planis thus $113,800.

It was observed that the above build plan results in excess supply ofseveral key components, in particular 3,800 units of 15″ XGA panels anda total of 3,100 units between the 20.0 GB and 40.0 GB hard drives. Inorder to better utilize the excess supply, it might be desirable tobuild and sell a product configuration made up of a 15″ XGA panel and a20.0 GB or 40.0 GB hard drive. However none of the original productconfigurations in Table 1 offer such a combination.

This observation helps establish a guideline for the new AdaptiveProduct Configuration Model. Based on business or technical designconsiderations, excess component inventory during the planning horizoncan be handled by intelligently expanding the set of productconfigurations. The Adaptive Product Configuration Model described inthis invention would determine new product configurations and the bestpossible build plan using various criteria as described on page 6.

For the above example, the Adaptive Product Configuration Model createsa set of three new product configurations, N1 to N3, shown in Table 5.All the components utilized in their bill-of-materials have excesssupply.

TABLE 5 Bill-of-materials data for the new product configurations. NewProduct Offerings PC Components N1 N2 N2 Panels 14″ XGA 15″ XGA 1 1 115″ SXGA+ Hard Drives 20.0 GB 1 1 40.0 GB 1 60.0 GB 80.0 GB Optical DVD1 1 1 Drives CD-RW Combo Wireless WiFi A 1 1 Hardware WiFi B 1

With the so expanded set of product configurations P1 to P 10 and N1 toN3, the Adaptive Product Configuration Model generates a new build planas displayed in Table 6.

TABLE 6 Build plan generated by the Adaptive Product ConfigurationModel. New Product Family F1 Family F2 Family F3 Offerings P1 P2 P3 P4P5 P6 P7 P8 P9 P10 N1 N2 N3 Demand 1000 1000 1000 1000 1000 1000 10001000 1000 1000 — — — Forecast Production — — 1000 1000 1000 1600 14001000 1000 1000 1200 1400 400 Plan Backorders 1000 1000 — — — — — — — — —— — Backorder $50,000 $50,000 — — — — — — — — — — — costs

Table 7 displays the consumed and excess component supply that resultsfrom the build plan shown in Table 6.

TABLE 7 Consumed supply, excess supply, and liability costs for thebuild plan generated by the Adaptive Product Configuration Model. SupplySupply Consumed Excess Liability PC Components Requirement CommitmentSupply supply costs Panels 14″ XGA 4,000 2,000 2,000 — — 15″ XGA 3,0008,000 7,000 1,000 $1,000 15″ SXGA+ 3,000 3,000 3,000 — — Hard Drives20.0 GB 3,000 3,600 3,600 — — 40.0 GB 1,000 1,500 1,400 100 $100 60.0 GB3,000 3,600 3,600 — — 80.0 GB 3,000 4,000 3,400 600 $600 Optical DVD3,000 4,000 4,000 — — Drives CD-RW 4,000 4,800 4,600 200 $200 Combo3,000 3,600 3,400 200 $200 Wireless WiFi A 5,000 6,500 6,000 500 $500Hardware WiFi B 5,000 6,000 6,000 — —

Once again assuming that the backorder cost is $50 and the liabilitycost is $1, the build plan in Table 6 produces a backorder cost of$100,000 and inventory liability costs of $2,600. The total cost of thebuild plan generated by the invention is thus $102,600 which in thisparticular instance represents a 9.8% reduction of total costs and an82.2% reduction of liability costs when compared to the conventionalmethod.

In recapitulation, and as illustrated in the example above, the AdaptiveProduct Configuration Model begins with an initial set of productconfigurations; their demand forecasts and component supply commitmentsfor a pre-determined planning horizon. Once a feasible production planfor the end products has been determined, it evaluates whether inventoryliability costs can be reduced by introducing new productconfigurations. This process is iterative and might take into accountone or more cost drivers as explained above.

Mathematical Formulation of the Problem

The Adaptive Product Configuration Model involves solving a masteroptimization problem and a dual optimization problem in an iterativealgorithm. The master problem develops an optimal build plan for arecommended set of configurations, including a set of original productconfigurations and zero or more new configurations. The dual problemdetermines the best new configuration to be added to the existing setsuch that an objective selected from a group of various criteria isoptimized. The following provides an exemplary embodiment of theinvention and describes a mathematical model involved in the AdaptiveProduct Configuration Model. It is assumed without loss of generalitythat the objective is to minimize the sum of liability costs andbackorder costs. Those skilled in the art will recognize that theinvention can be practiced with modification within and scope of theappended claims.

The following notation will be used to describe the model formulationand data:

-   -   I: set of components, indexed by i.    -   S: set of commodities, or component groups, indexed by s.    -   M: set of existing product configurations indexed by m.    -   N: set of recommended new configurations, indexed by n. The        cardinality of this set will increase during the solution        process.    -   r[i,m]: usage rate of component i in configuration m.    -   g[i,s]: relationship between component i and commodity s.        g[i,s]=1 if component i belongs to commodity s; 0 otherwise.    -   C_(h)[i]: liability cost per unit of excess supply of component        i.    -   C_(b)[m]: backorder cost per unit of product configuration m.    -   C_(o)[m]: overproduction cost per unit of product configuration        m.    -   C_(p)[n]: product release cost per unit of new configuration n.    -   C_(s)[m, m′]: cost of substituting product m′ to satisfy demand        for product m.    -   d[m]: demand forecast for product configuration m.    -   b[m]: backorder quantity of product configuration m.    -   α: demand upside potential, or maximum percentage of        overproduction.    -   w[i]: supply-committed inventory of component i.    -   w_(U)[i],w_(L)[i]: upper and lower bounds of supply-committed        inventory of component i.    -   q[i]: on hand inventory of component i.    -   T[m,m′]: product substitution matrix; T[m,m′]=1 if product        configuration m can be substituted by product configuration m′;        0 otherwise.    -   x[m,m′]: quantity of product m′ produced to satisfy demand for        product m.    -   z[m]: amount of product m overproduced, i.e., the amount        exceeding the demand forecast of product m.    -   r_(n)[i, n]: usage rate of component i in new product        configuration n; each column of this matrix represents a new        configuration.    -   X[m]: build quantity of existing product configuration m.    -   Y[n]: build quantity of new product configuration n.

With the notation defined above, the master optimization problem isintroduced as follows:

$\begin{matrix}{{Master}\mspace{14mu}{problem}} & \; \\{\quad{{{Min}{\sum\limits_{m \in M}\;( {{\sum\limits_{m^{\prime} \in M}\;{{C_{S}\lbrack {m,m^{\prime}} \rbrack}{x\lbrack {m,m^{\prime}} \rbrack}}} + {\sum\limits_{m^{\prime} \in M}\;{{C_{S}\lbrack {m^{\prime},m} \rbrack}{x\lbrack {m^{\prime},m} \rbrack}}}} )}} + {\sum\limits_{m \in M_{0}}\;{{C_{b}\lbrack m\rbrack}{b\lbrack m\rbrack}}} + {\sum\limits_{i \in I}\;{{C_{h}\lbrack i\rbrack}( {{w\lbrack i\rbrack} + {q\lbrack i\rbrack} - {\sum\limits_{m \in M}\;{{r\lbrack {i,m} \rbrack}{X\lbrack m\rbrack}}} - {\sum\limits_{n \in N}\;{{r_{n}\lbrack {i,n} \rbrack}{Y\lbrack n\rbrack}}}} )}} + {\sum\limits_{m \in M}\;{{C_{O}\lbrack m\rbrack}{z\lbrack m\rbrack}}} + {\sum\limits_{n \in N}\;{{C_{p}\lbrack n\rbrack}{Y\lbrack n\rbrack}}}}\mspace{635mu}} & (1) \\{{Subject}\mspace{14mu}{to}} & \; \\{{{d\lbrack m\rbrack} - {\sum\limits_{m^{\prime} \in M}\;{{x\lbrack {m,m^{\prime}} \rbrack}{T\lbrack {m,m^{\prime}} \rbrack}}} + {\sum\limits_{m^{\prime} \in M}\;{{x\lbrack {m^{\prime},m} \rbrack}{T\lbrack {m^{\prime},m} \rbrack}}} - {b\lbrack m\rbrack} + {z\lbrack m\rbrack}} = {{{X\lbrack m\rbrack} \geq {0\mspace{14mu}{for}\mspace{14mu}{all}\mspace{14mu} m}} \in M}} & (2) \\{\mspace{140mu}{{{( {1 + \alpha} ){\sum\limits_{m \in M}\;{d\lbrack m\rbrack}}} - {\sum\limits_{m \in M}\;{X\lbrack m\rbrack}} - {\sum\limits_{n \in N}\;{Y\lbrack n\rbrack}}} \geq 0}\;} &  3 ) \\{\mspace{20mu}{{{{w\lbrack i\rbrack} + {q\lbrack i\rbrack} - {\sum\limits_{m \in M}\;{{r\lbrack {i,m} \rbrack}{X\lbrack m\rbrack}}} - {\sum\limits_{n \in N}\;{{r_{n}\lbrack {i,n} \rbrack}{y\lbrack n\rbrack}}}} \geq {0\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i}} \in I}} & (4) \\{\mspace{256mu}{{{w\lbrack i\rbrack} \leq {{w_{U}\lbrack i\rbrack}\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i}} \in I}} & (5) \\{\mspace{256mu}{{{w\lbrack i\rbrack} \geq {{w_{L}\lbrack i\rbrack}\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i}} \in I}} & (6)\end{matrix}$

The five summation terms in (1) are the costs for substitutions, thecosts for backlogging demand, the costs for holding inventory, the costsfor overproducing existing products, and the costs for producing newconfigurations, respectively. Constraint (2) ensures that the productionquantity for an existing product configuration is always non-negative.Constraint (3) limits the total build quantity of all existing and newproduct configurations such that it does not exceed a certain tolerance(the demand upside potential) over and above the original demandforecast. Constraint (4) makes sure that the required quantity for acomponent does not exceed the available supply for this component.Constraints (5) and (6) represent contractual agreements describingupside and downside volume flexibility of supply-committed componentinventories.

The master problem can be solved in various ways, including linearprogramming and heuristic techniques (e.g. local search techniques).After the master problem is solved, it is straightforward to obtain thedual variables from the solution directly. For this purpose, let π bethe dual variable associated with constraint (3), and let λ[i] be thedual variable associated with constraint set (4).

Before the dual problem is defined, the following additional variablesare introduced.

-   -   K[i]: bill-of-materials of new product configuration; K[i]=1 if        the new product configuration uses component i; 0 otherwise.

With the notation defined above, the dual optimization problem isintroduced as follows.

$\begin{matrix}{{{Dual}\mspace{14mu}{problem}}\mspace{520mu}} & \; \\{\mspace{220mu}{{{Min}{\sum\limits_{i \in I}\;{( {{- {C_{h}\lbrack i\rbrack}} + {\lambda\lbrack i\rbrack}} ){K\lbrack i\rbrack}}}} + \pi}\mspace{220mu}} & {(7)\mspace{14mu}} \\{{{Subject}\mspace{14mu}{to}}\mspace{554mu}} & \; \\{{\sum\limits_{i \in I}\;{{K\lbrack i\rbrack}{g\lbrack {i,s} \rbrack}}} = {{1{\mspace{14mu}\;}{for}\mspace{14mu}{all}{\mspace{11mu}\;}s} \in S}} & {(8)\mspace{14mu}} \\{{K\lbrack i\rbrack} \in {\{ {0,1} \}\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i} \in I} & {(9)\mspace{14mu}}\end{matrix}$

Constraints (8) and (9) reflect the fact that a new productconfiguration must contain a squared set of components, meaning that itsbill-of-materials must contain exactly one component i from eachcommodity group s (for example, one hard drive, one panel, etc.).

The dual problem is a 0-1 integer programming problem and can be solvedby conventional integer programming tools as well as various heuristictechniques or artificial intelligence techniques. After the dual problemis solved, the optimized objective value (7) is evaluated. If it isnegative, the new product configuration is added to the current set ofconfigurations and the master problem (I) is solved over again.Otherwise the algorithm has reached optimality.

While the invention has been described in terms of a single preferredembodiment, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims.

1. A computer-implemented method comprising the steps of: inputting intoone or more computers an inventory statement which comprises a pluralityof existing product configurations and a plurality of componentscorresponding to each of said plurality of existing productconfigurations; inputting into said one or more computers productconfiguration rules which govern assembly of one or more of saidplurality of components into product configurations which include saidplurality of existing product configurations; applying, with said one ormore computers, the product configuration rules to an excess of at leastone component of at least one existing product configuration of saidplurality of existing product configurations of said inventorystatement; generating, with said one or more computers, a build plan forsaid product configurations which includes one or more of said pluralityof existing product configurations and at least one new productconfiguration which is different from each of said plurality of existingproduct configurations which consumes at least one of said excess ofsaid at least one component from said inventory statement, wherein saidstep of generating the build plan utilizes an integrated masteroptimization problem solving and dual optimization problem solvingstrategy in said one or more computers, wherein said integrated masteroptimization problem solving and dual optimization problem solvingstrategy accounts for a) costs for substitutions, costs for backloggingdemand, costs for holding inventory, costs for overproducing existingproduct configurations, and costs for producing said new productconfigurations; b) ensuring production quantity for an existing productconfiguration is always non-negative; c) limiting total build quantityof said existing product configurations and said new productconfigurations to a specified tolerance over an original demandforecast; d) assuring a required quantity of said at least one componentdoes not exceed a supply of said at least one component; e) conformingto contractual agreements on supply-committed component inventories; andf) assuring said at least one new product configuration contains asquared set of components.
 2. The method according to claim 1, includinga step of inputting into said one or more computers a sales plan of saidat least one of said existing product configuration and a productsubstitution plan.
 3. The method according to claim 2, wherein said stepof generating said build plan includes at least one of a criterionselected from a group comprising: maximizing revenue of said build plan;maximizing profitability of said build plan; minimizing liability costsfor under-utilizing said inventory statement; minimizing penalty costsfor violating desired customer services levels; minimizing penalty costsfor deviating from sales plan of said set of existing productconfigurations; and maximizing a goodness value function of said productconfigurations.
 4. The method of claim 3 wherein said goodness valuefunction utilizes at least one of: profitability of said productconfigurations; competitive advantage gain of said productconfiguration; marketability of said product configurations;compatibility of said product configurations with said existing productconfigurations; and cannibalization of said new product configurationwith said existing product configurations.
 5. The method of claim 3wherein generating a build plan includes a step of formulatingconstraints utilizing at least one of: contractual agreements describingupside and downside volume flexibility of supply-committed componentinventories; and upside demand potential relative to a top-level salesplan.
 6. The method of claim 3, wherein said step of generating a buildplan comprises a relationship: $\begin{matrix}{{{Min}{\sum\limits_{m \in M}\;( {{\sum\limits_{m^{\prime} \in M}\;{{C_{S}\lbrack {m,m^{\prime}} \rbrack}{x\lbrack {m,m^{\prime}} \rbrack}}} + {\sum\limits_{m^{\prime} \in M}\;{{C_{S}\lbrack {m^{\prime},m} \rbrack}{x\lbrack {m^{\prime},m} \rbrack}}}} )}} +} & (1) \\{{\sum\limits_{m \in M_{0}}\;{{C_{b}\lbrack m\rbrack}{b\lbrack m\rbrack}}} +} & \; \\{{\sum\limits_{i \in I}\;{{C_{h}\lbrack i\rbrack}( {{w\lbrack i\rbrack} + {q\lbrack i\rbrack} - {\sum\limits_{m \in M}\;{{r\lbrack {i,m} \rbrack}{X\lbrack m\rbrack}}} - {\sum\limits_{n \in N}\;{{r_{n}\lbrack {i,n} \rbrack}{Y\lbrack n\rbrack}}}} )}} +} & \; \\{\mspace{405mu}{{\sum\limits_{m \in M}\;{{C_{O}\lbrack m\rbrack}{z\lbrack m\rbrack}}} + {\sum\limits_{n \in N}\;{{C_{p}\lbrack n\rbrack}{Y\lbrack n\rbrack}}}}} & \; \\{{{subject}\mspace{14mu}{to}\text{:}}\mspace{625mu}} & \; \\{{{d\lbrack m\rbrack} - {\sum\limits_{m^{\prime} \in M}\;{{x\lbrack {m,m^{\prime}} \rbrack}{T\lbrack {m,m^{\prime}} \rbrack}}} + {\sum\limits_{m^{\prime} \in M}\;{{x\lbrack {m^{\prime},m} \rbrack}{T\lbrack {m^{\prime},m} \rbrack}}} -}\mspace{115mu}} & (2) \\{\mspace{329mu}{{{b\lbrack m\rbrack} + {z\lbrack m\rbrack}} = {{{X\lbrack m\rbrack} \geq {0\mspace{14mu}{for}\mspace{14mu}{all}\mspace{14mu} m}} \in M}}} & \; \\{{{{( {1 + \alpha} ){\sum\limits_{m \in M}\;{d\lbrack m\rbrack}}} - {\sum\limits_{m \in M}\;{X\lbrack m\rbrack}} - {\sum\limits_{n \in N}\;{Y\lbrack n\rbrack}}} \geq 0}\;} & (3) \\{{{{w\lbrack i\rbrack} + {q\lbrack i\rbrack} - {\sum\limits_{m \in M}\;{{r\lbrack {i,m} \rbrack}{X\lbrack m\rbrack}}} - {\sum\limits_{n \in N}\;{{r_{n}\lbrack {i,n} \rbrack}{y\lbrack n\rbrack}}}} \geq {0\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i}} \in I} & (4) \\{{{w\lbrack i\rbrack} \leq {{w_{U}\lbrack i\rbrack}\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i}} \in I} & (5) \\{{{w\lbrack i\rbrack} \geq {{w_{L}\lbrack i\rbrack}\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i}} \in I} & (6)\end{matrix}$ wherein, a set of variables of said relationship includes:I as the set of components, indexed by i, S as the set of commodities,or component groups, indexed by s, M as the set of existing productconfigurations indexed by m, N as the set of recommended newconfigurations, indexed by n, wherein, the cardinality of this set willincrease during the solution process, r[i,m] is the usage rate ofcomponent i in configuration m, g[i,s] is the relationship betweencomponent i and commodity s, wherein g[i,s]=1 if component i belongs tocommodity s; 0 otherwise, C_(h)[i] is the liability cost per unit ofexcess supply of component i, C_(b)[m] is the backorder cost per unit ofproduct configuration m, C_(o)[m] is the overproduction cost per unit ofproduct configuration m, C_(p)[n] is the product release cost per unitof new configuration n, C_(s)[m, m′] is the cost of substituting productm′ to satisfy demand for product m, d[m] is the demand forecast forproduct configuration m, b[m] is the backorder quantity of productconfiguration m, α is the demand upside potential, or maximum percentageof overproduction, w[i] is the supply-committed inventory of componenti, w_(U)[i] ,w_(L)[i] are the upper and lower bounds of supply-committedinventory of component i, q[i] is the on hand inventory of component i,T[m,m′] is the product substitution matrix; T[m,m′]=1 if productconfiguration m can be substituted by product configuration m′; 0otherwise, x[m,m′] is the quantity of product m′ produced to satisfydemand for product m, z[m] is the amount of product m overproduced,i.e., the amount exceeding the demand forecast of product m, r_(n)[i, n]is the usage rate of component i in new product configuration n; eachcolumn of this matrix represents a new configuration, X[m] is the buildquantity of existing product configuration m, and Y[n] is the buildquantity of new product configuration n.
 7. The method according toclaim 6, wherein said relationship comprises: $\begin{matrix}{{{{{Min}{\sum\limits_{i \in I}\;{( {{- {C_{h}\lbrack i\rbrack}} + {\lambda\lbrack i\rbrack}} ){K\lbrack i\rbrack}}}} + \pi}{subject}\mspace{14mu}{to}\text{:}}\mspace{610mu}} & (7) \\{{\sum\limits_{i \in I}\;{{K\lbrack i\rbrack}{g\lbrack {i,s} \rbrack}}} = {{1{\mspace{14mu}\;}{for}\mspace{14mu}{all}{\mspace{11mu}\;}s} \in S}} & (8) \\{{K\lbrack i\rbrack} \in {\{ {0,1} \}\mspace{20mu}{for}\mspace{14mu}{all}\mspace{14mu} i} \in I} & (9)\end{matrix}$ wherein, said set of variables of said relationshipincludes, K[i]: bill-of-materials of new product configuration; K[i]=1if the new product configuration uses component i; 0 otherwise.